In grasp detection, the robot estimates the position and orientation of potential grasp configurations directly from sensor data. This paper explores the relationship between viewpoint and grasp detection performance. Specifically, we consider the scenario where the approximate position and orientation of a desired grasp is known in advance and we want to select a viewpoint that will enable a grasp detection algorithm to localize it more precisely and with higher confidence. Our main findings are that the right viewpoint can dramatically increase the number of detected grasps and the classification accuracy of the top-n detections. We use this insight to create a viewpoint selection algorithm and compare it against a random viewpoint selection strategy and a strategy that views the desired grasp head-on. We find that the head-on strategy and our proposed viewpoint selection strategy can improve grasp success rates on a real robot by 8% and 4%, respectively. Moreover, we find that the combination of the two methods can improve grasp success rates by as much as 12%.
翻译:在抓取检测中, 机器人直接从感官数据中估算潜在抓取配置的位置和方向。 本文探讨观点和抓取检测性能之间的关系。 具体地说, 我们考虑预知预知预想抓取的近似位置和方向的情景, 我们想要选择一个能够更精确和更自信地定位它的观点。 我们的主要发现是, 正确的观点可以大幅提高被检测到的抓取数量和顶级检测的分类准确性 。 我们使用这种洞察来创建观点选择算法, 并比对随机观点选择策略和观察预想抓取效果的战略。 我们发现, 头部策略和我们拟议的观点选择战略可以将真实机器人的成功率分别提高8%和4%。 此外, 我们发现, 这两种方法的组合可以将成功率提高12%。